Artificial Penetration Software Solutions Data Scientist Interview Questions and Answers

Q1. Basic ml related questions,past project discussion
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Q1. AI, ML related, statistical analysis of the graph.
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Q1. Work culture, check
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Q1. Work culture, project discussion, scenario based questions
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Q1. Basic HR questions, why sap salary discussions
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Q1. Basic DS question like how to handle missing features
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Q1. Case study based questions
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Q1. Basic pandas questions on dataframes
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Q2. Some quiz questions
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Q1. How can Logistic regression be applied for multiclasstext classification
Ans. 
Logistic regression can be applied for multiclasstext classification by using one-vs-rest or softmax approach.
• One-vs-rest approach: Train a binary logistic regression model for each class, treating it as the positive class and the rest as the negative class.

• Softmax approach: Use the softmax function to transform the output of the logistic regression into probabilities for each class.

• Evaluate the model using appropriate...read more
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Q1. How to ensure scalability
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45 mins.

questions based on basic string manipulation but not a straightforward task

Q1. Given a situation how do you handle different cases
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Q2. Given a proab stats question
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Q1. Explain L1 & L2 regularization
Ans. 
L1 & L2 regularization are techniques used in machine learning to prevent overfitting by adding a penalty term to the cost function.
• L1 regularization adds the absolute values of the coefficients as penalty term (Lasso regression)

• L2 regularization adds the squared values of the coefficients as penalty term (Ridge regression)

• L1 regularization encourages sparsity in the model, while L2 regularization tends to shrink the c...read more
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Q2. Explain error metric
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Error metric is a measure used to evaluate the performance of a model by comparing predicted values to actual values.
• Error metric quantifies the difference between predicted values and actual values.

• Common error metrics include Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared.

• Lower values of error metric indicate better performance of the model.

• Error metric helps in und...read more
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Q3. Where do you see yourself
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